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NONPARAMETRIC STOCHASTIC VOLATILITY

Published online by Cambridge University Press:  03 July 2018

Federico M. Bandi
Affiliation:
Johns Hopkins University and Edhec-Risk Institute
Roberto Renò*
Affiliation:
Università di Verona
*
*Address correspondence to Roberto Renò, Università di Verona, Via Cantarane 24, 37129 Verona, Italy; e-mail: roberto.reno@univr.it.

Abstract

We provide nonparametric methods for stochastic volatility modeling. Our methods allow for the joint evaluation of return and volatility dynamics with nonlinear drift and diffusion functions, nonlinear leverage effects, and jumps in returns and volatility with possibly state-dependent jump intensities, among other features. In the first stage, we identify spot volatility by virtue of jump-robust nonparametric estimates. Using observed prices and estimated spot volatilities, the second stage extracts the functions and parameters driving price and volatility dynamics from nonparametric estimates of the bivariate process’ infinitesimal moments. For these infinitesimal moment estimates, we report an asymptotic theory relying on joint in-fill and long-span arguments which yields consistency and weak convergence under mild assumptions.

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ARTICLES
Copyright
Copyright © Cambridge University Press 2018 

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Supplementary material: PDF

Bandi and Renò supplementary material 1

Appendix

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